Thanks a lot! Great job, Team! On Fri, Nov 17, 2023 at 7:21 PM Danny McCormick via user < user@beam.apache.org> wrote:
> I am happy to announce that the 2.52.0 release of Beam has been finalized. > This release includes both improvements and new functionality. > > For more information on changes in 2.52.0, check out the detailed release > notes - https://github.com/apache/beam/milestone/16. Here is an overview > of the changes in the release. > > Highlights > > * Previously deprecated Avro-dependent code (Beam Release 2.46.0) has been > finally removed from Java SDK "core" package. Please, use > `beam-sdks-java-extensions-avro` instead. This will allow to easily update > Avro version in user code without potential breaking changes in Beam "core" > since the Beam Avro extension already supports the latest Avro versions and > should handle this. (https://github.com/apache/beam/issues/25252). > * Publishing Java 21 SDK container images now supported as part of Apache > Beam release process. (https://github.com/apache/beam/issues/28120) > * Direct Runner and Dataflow Runner support running pipelines on Java21 > (experimental until tests fully setup). For other runners (Flink, Spark, > Samza, etc) support status depend on runner projects. > > New Features / Improvements > > * Add `UseDataStreamForBatch` pipeline option to the Flink runner. When it > is set to true, Flink runner will run batch jobs using the DataStream API. > By default the option is set to false, so the batch jobs are still executed > using the DataSet API. > * `upload_graph` as one of the Experiments options for DataflowRunner is > no longer required when the graph is larger than 10MB for Java SDK ( > https://github.com/apache/beam/pull/28621). > * state amd side input cache has been enabled to a default of 100 MB. Use > `--max_cache_memory_usage_mb=X` to provide cache size for the user state > API and side inputs. (Python) (https://github.com/apache/beam/issues/28770 > ). > * Beam YAML stable release. Beam pipelines can now be written using YAML > and leverage the Beam YAML framework which includes a preliminary set of > IO's and turnkey transforms. More information can be found in the YAML root > folder and in the ( > https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/README.md > ). > > Breaking Changes > > * `org.apache.beam.sdk.io.CountingSource.CounterMark` uses custom > `CounterMarkCoder` as a default coder since all Avro-dependent classes > finally moved to `extensions/avro`. In case if it's still required to use > `AvroCoder` for `CounterMark`, then, as a workaround, a copy of "old" > `CountingSource` class should be placed into a project code and used > directly > (https://github.com/apache/beam/issues/25252). > * Renamed `host` to `firestoreHost` in `FirestoreOptions` to avoid > potential conflict of command line arguments (Java) ( > https://github.com/apache/beam/pull/29201). > > Bugfixes > > * Fixed "Desired bundle size 0 bytes must be greater than 0" in Java SDK's > BigtableIO.BigtableSource when you have more cores than bytes to read > (Java) (https://github.com/apache/beam/issues/28793). > * `watch_file_pattern` arg of the RunInference arg had no effect prior to > 2.52.0. To use the behavior of arg `watch_file_pattern` prior to 2.52.0, > follow the documentation at > https://beam.apache.org/documentation/ml/side-input-updates/ and use > `WatchFilePattern` PTransform as a SideInput. ( > https://github.com/apache/beam/pulls/28948) > * `MLTransform` doesn't output artifacts such as min, max and quantiles. > Instead, `MLTransform` will add a feature to output these artifacts as > human readable format - (https://github.com/apache/beam/issues/29017). > For now, to use the artifacts such as min and max that were produced by the > eariler `MLTransform`, use `read_artifact_location` of `MLTransform`, which > reads artifacts that were produced earlier in a different `MLTransform` ( > https://github.com/apache/beam/pull/29016/) > * Fixed a memory leak, which affected some long-running Python pipelines: ( > https://github.com/apache/beam/issues/28246). > > Security Fixes > > * Fixed CVE-2023-39325 - (https://www.cve.org/CVERecord?id=CVE-2023-39325) > (Java/Python/Go) (https://github.com/apache/beam/issues/29118). > * Mitigated CVE-2023-47248 - ( > https://nvd.nist.gov/vuln/detail/CVE-2023-47248) (Python) ( > https://github.com/apache/beam/issues/29392). > > Thanks, > Danny >
